Although this invention is being disclosed in connection with cervical cancer, it is applicable to many other areas of medicine. Uterine cervical cancer is the second most common cancer in women worldwide, with nearly 500,000 new cases and over 270,000 deaths annually (Globocan 2002 database, International agency for research in cancer, 2002, incorporated herein by reference). Colposcopy is a diagnostic method used to detect cancer precursors and cancer of the uterine cervix (B. S. Apgar, Brotzman, G. L. and Spitzer, M., Colposcopy: Principles and Practice, W.B. Saunders Company: Philadelphia, 2002, incorporated herein by reference). CAD for colposcopy represents a new application of medical image processing. The inventors have developed a CAD system that mimics or emulates the diagnostic process used by colposcopists to assess the severity of abnormalities (Lange H. and Ferris, Daron G.; Computer-Aided-Diagnosis (CAD) for colposcopy; SPIE Medical Imaging 2005; SPIE Proc. 5747, 2005, incorporated herein by reference). Scoring schemes, like the Reid's colposcopic index are an aid for making colposcopic diagnoses (Reid R, Scalzi P. Genital warts and cervical cancer. VII. An improved colposcopic index for differentiating benign papillomaviral infection from high-grade cervical intraepithelial neoplasia. Am J Obstet Gynecol 1985; 153:611-618, incorporated herein by reference) based on various features, including acetowhitening, vessel patterns and lesion margins. These features are individually assessed and scored before the scores of all features are combined to yield a composite score that grades disease severity. However, the quality of the images must be assessed before further analysis, to ensure reliable scoring. This invention includes a systematic framework of algorithms that automatically assesses cervical images acquired from a digital colposcope. This assessment results in a filtered dataset of images that can then be used for CAD algorithms. This invention can be used to control image acquisition, which guarantees quality of input imagery for CAD systems and archive-quality medical records, and can also be used in telemedicine cervical cancer diagnosis.
The limited quality of cervical imagery can be attributed to several factors, including: incorrect instrument settings, incorrect instrument positioning, glint, blur due to poor focus, and physical contaminants. Glint (specular reflection) eliminates the color information in affected pixels and can therefore introduce artifacts in feature extraction algorithms. Specular reflection is perfect, mirror-like reflection of light from a surface, in which light from a single incoming direction (a ray) is reflected into a single outgoing direction. A pixel is a single point in a graphic image and is the smallest single element of an image. Each pixel in an image has its own value that correlates to its brightness or intensity. In a color image, each pixel can be described using its hue, saturation, and value (HSV) or hue, saturation, lightness (HSL), but is usually represented instead as the red, green, and blue (RGB) intensities. Hue, saturation, and intensity (HSI) and hue, saturation, and brightness (HSB) are alternative names for HSV and HSL. HSL and HSV can be used to represent colors as points in a cylinder whose central axis ranges from black at the bottom to white at the top with neutral colors between them, where the angle around the axis corresponds to “hue”, distance from the axis corresponds to “saturation”, and distance along the axis corresponds to “lightness”, “value”, and “brightness”. Instrument settings that result in an inadequate dynamic range (defined below) or overly constrained (too small) region of interest can reduce or eliminate pixel information and thus make image analysis algorithms unreliable. Poor focus causes image blur with a consequent loss of texture information. In addition, a variety of physical contaminants, such as blood, can obscure the desired scene, and reduce or eliminate diagnostic information from affected areas.
The present invention proposes a series of image quality assessment algorithms called Active Image Quality Assessment (AIQA), which include locating a region of interest, region assessment of the image, contrast assessment of the image, blur assessment of the image and contamination detection. These algorithms are specifically designed for cervical imaging, but can be applied to other types of tissue imaging as well. While many of the algorithms described herein are well-known in the art, the inventors are unaware of any other image processing method that uses the specific blur assessment algorithm of this invention, or its application to CAD technology. The following patents and patent applications may be considered relevant to the field of the invention:
U.S. Pat. No. 7,298,883 to Giger et al., incorporated herein by reference, discloses a computer-aided diagnosis (CAD) scheme to aid in the detection, characterization, diagnosis, and/or assessment of normal and diseased states (including lesions and/or images). The scheme employs lesion features for characterizing the lesion and includes a non-parametric classification, to aid in the development of CAD methods in a limited database scenario to distinguish between malignant and benign lesions. The non-parametric classification is robust to kernel size.
U.S. Pat. No. 7,272,252 to De La Torre-Bueno and McBride, incorporated herein by reference, discloses a method and apparatus for automated analysis of transmitted and fluorescently labeled biological samples, wherein the apparatus automatically scans at a low magnification to acquire images which are analyzed to determine candidate cell objects of interest. Once candidate objects of interest are identified, further analysis is conducted automatically to process and collect data from samples having different staining agents.
U.S. Patent Publication No. 2007/0019854 to Gholap; Abhijeet S. et al., incorporated herein by reference, discloses a method and system of automated digital image analysis of prostrate neoplasms using morphologic patterns. The method and system provide automated screening of prostate needle biopsy specimens in a digital image and automated diagnosis of prostatectomy specimens.
U.S. Patent Publication No. 2005/0251013 to Krishnan, et al., incorporated herein by reference, discloses systems and methods for processing a medical image to automatically identify the anatomy and view (or pose) from the medical image and automatically assess the diagnostic quality of the medical image. In one aspect a method for automated decision support for medical imaging includes obtaining image data, extracting feature data from the image data, and automatically performing anatomy identification, view identification and/or determining a diagnostic quality of the image data, using the extracted feature data.
U.S. Patent Publication No. 2005/0049497 to Krishnan, et al., incorporated herein by reference, discloses CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.
U.S. Pat. No. 6,813,374 to Karimi et al., incorporated herein by reference, discloses a method and apparatus to assess the image quality of a CT scanner and verify that a CT scanner meets it is performance specifications.
U.S. Pat. No. 6,687,329 to Hsieh et al. discloses a method for processing image data comprising: (a) acquiring first image data via an imaging system; (b) processing the first image data in accordance with a CAD algorithm, the CAD algorithm performing at least one of segmenting, identifying and classifying a feature of interest in the first image data; and (c) prescribing acquisition of at least second image data based upon results of the CAD algorithm.
U.S. Patent Publication No. 2004/006,8167 to Hsieh, Jiang et al., incorporated herein by reference, discloses a method and system for generating processing image data based on the analysis of an initial image by a CAD algorithm which may perform various analyses such as segmentation, edge and structure identification. The post-processing may enhance a feature of interest in the image as identified by the CAD analysis. Image enhancement may include highlighting a feature of interest and changing the spatial resolution (e.g. zoom).
U.S. Pat. No. 6,147,705 to Krauter et al., incorporated herein by reference, discloses an apparatus and method for a video colposcope with electronic green filter. A video camera obtains a subject electronic image of a subject object, and using algorithm-driven digital signal processing circuitry (DSP), color saturation, hue, and intensity levels of the subject electronic image are modified according to DSP reference filter algorithm and reference color balance levels as stored, thus producing a modified electronic image corresponding to the subject electronic image. The modified electronic image is outputted to a display in continuous real time as the corresponding subject image is obtained by the video camera. This modified electronic image emulates that obtained through an optical green filter and incorporates a simulated white balance.
U.S. Pat. No. 5,982,917 to Clarke, et al., incorporated herein by reference, discloses a computer-assisted diagnostic (CAD) method and apparatus are described for the enhancement and detection of suspicious regions in digital X-ray images, with particular emphasis on early cancer detection using digital mammography. One objective is to improve the sensitivity of detection of suspicious areas such as masses, while maintaining a low false positive detection rate, and to classify masses as benign or malignant. A modular CAD technique has been developed as a potentially automatic and/or second-opinion method for mass detection and classification in digital mammography that may in turn be readily modified for application with different digital X-ray detectors with varying gray-scale and resolution characteristics. The method consists of using a plurality of CAD modules to preprocess and enhance image features in the gray-level, the directional texture, and the morphological domains.
U.S. Pat. No. 5,740,801 to Branson, incorporated herein by reference, discloses a system for acquiring images during a medical procedure and using the acquired images which includes a storage device for storing, for each one of a plurality of users of the system, or for each one of a plurality of medical procedures, or for each one of a plurality of input or output devices, information that indicates one or more processing operations to be performed on images obtained by an input device. A system processor responds to an identity the user who is currently using the system by performing processing operations on the obtained images and applying the images to an output device based on the stored information that corresponds to the current user.